The invention relates generally to a device for predicting a fault condition associated with a machine, and more particularly, to a method and apparatus for predicting a fault condition in response to the trend of a machine parameter using non-linear curve fitting techniques.
For service and diagnostic purposes, machines are sometimes equipped with sensors for measuring operating conditions such as engine RPM, oil pressure, water temperature, boost pressure, oil contamination, electric motor current, hydraulic pressure, system voltage, and the like. In some cases, storage devices are provided to compile a data base for later evaluation of machine performance and to aid in diagnosis. Service personnel examine the accrued data to get a better picture of the causes of any machine performance degradation, wear or failure. Similarly, service personnel evaluate the stored data to predict future failures and associated collateral damages, and to correct any problems before total component failure.
In addition, these stored parameters may be examined by service or supervisory personnel to evaluate machine and/or operator performance to ensure maximum productivity of the machine. These issues are particularly pertinent to over-the-highway trucks and large work machines such as off-highway mining trucks, hydraulic excavators, track-type tractors, wheel loaders, and the like. These machines represent large capital investments and are capable of substantial productivity when operating. It is therefore important to predict significant performance loss, wear and catastrophic failures so servicing can be scheduled during periods in which productivity will be less affected and so minor problems can be repaired before they lead to catastrophic failures.
Similarly, it is sometimes advantageous to accumulate parameters only when the machine is in a particular operating condition. This type of information is predominantly used during performance evaluation but may also be used in failure diagnosis and prognosis. For example, the length of time spent in a particular gear while the machine is loaded may be needed to evaluate machine performance.
Currently, numerous methods and apparatus are known for predicting and diagnosing machine fault conditions. Reference, for instance, Schricker et al. U.S. Pat. No. 5,561,610, issued Oct. 1, 1996 to Caterpillar, Inc. which discloses linear curve fitting techniques for predicting fault conditions.
However, it has been observed that many available machine data streams are not best characterized utilizing linear functions, which results in less than optimal accuracy when it is attempted to predict and/or diagnose such fault conditions using the known linear techniques.
Accordingly, the present invention is directed to overcoming one or more of the problems set forth above.
An apparatus for predicting a fault condition for a machine is disclosed. The machine has a plurality of parameters being dependent upon machine performance. A sensor is connected to the machine and is adapted to produce an electrical signal in response to one of the plurality of machine parameters. The apparatus further includes means for determining a non-linear data trend of the parameter in response to the electrical signal, and means for predicting the fault condition as a function of the trend and a threshold value indicative of the fault condition.
In a second aspect of the invention, a method for predicting a fault condition is provided. The method includes the steps of sensing a parameter having a level being dependent upon machine performance and responsively producing an electrical signal, determining a non-linear data trend of the parameter in response to the electrical signal, and predicting the fault condition as a function of the trend and threshold value indicative of the fault condition.